The present invention relates generally to the field of measuring familiarity, and more particularly to measuring the probabilistic familiarity of artifacts.
Bayesian statistics is a known statistical field where information is expressed in terms of degrees of belief or, more specifically, probabilities. In Bayesian statistics, a prior probability distribution represents the probability of certain events occurring before some new evidence is taken into account, and a posterior probability distribution represents the conditional probability of the same events occurring after the new evidence is taken into account.
Bayesian surprise is known. Bayesian surprise is the quantification of the difference (or change) between a prior probability distribution and a corresponding posterior probability distribution. Bayesian surprise can be used in machine learning to determine how novel (or surprising) a new item (or “artifact”) is given a known dataset of existing items. One way to quantify the difference between a prior probability distribution and a corresponding posterior probability distribution is by calculating a Kullback-Leibler divergence, which is a measure of the information gained by moving from the prior probability distribution to the posterior probability distribution.